This Phase II SBIR research is the continuation of a Biodefense funded Phase I with the objective to develop new computational tools to discover host-pathogen genetic interactive mechanisms and create mathematical models for analyzing and predicting innate and adaptive immunity. Having new computational methods for identifying genetically regulated host immune response will significantly aid in advancing our understanding of the molecular targets and immunologic mechanisms critical to robust defense against pathogenic microbes. This is extremely important knowledge for biowarfare agents (BWA) and other common diseases of high public health concern especially for the safe and effective development of new vaccines and immunotherapeutic drugs. Further, new computational tools to help transform volumes of raw genomic/proteomic and physiologic data to actionable knowledge will help guide and accelerate researchers' investigations for new vaccines, adjuvants, and immunotherapeutic drugs. This is relevant, not only to biodefense, but for many other pathogens and diseases -- especially in studies where it is important to eventually predict human response from animal models. Phase I produced excellent results in demonstrating that our computational tools based on dynamic Bayesian networks (DBNs) can be used to discover mechanistic processes and model the host immune response to BWAs and others infectious diseases. As in Phase I, our Phase II computational tools will be based on the statistical/probabilistic power of DBNs which we plan to expand to enable and validate for multi-conditional comparative studies leading to mechanistic discovery and creation of predictive immune response models. These models are derived from the time-course patterns of genes, proteins and physiologic factors which we call the host's "biosignature". An innovation of our computational approach is the ability to include time-course data (gene, protein, and physiologic data) fused with prior biological knowledge (i.e. regulatory pathways, gene-gene/protein-protein relations, gene homologies, functional ontologies, etc.) which we believe significantly advances the discovery of novel immunologic components and the creation of mechanistic-based immune response models. The Phase II goals are to: 1) implement an innovative multi-conditional comparative computational methodology designed to enable the discovery of underlying host-pathogen genetic mechanisms, and 2) the validation of our mechanistic discovery and modeling methodology on a select set of pathogens (B. melitensis (Brucellosis), B. anthracis (anthrax), Coccidioides immitis (Valley fever), and Influenza) and vaccines (genetically engineered and attenuated). The investigation of new vaccines and drugs are producing huge volumes of "raw" immunologic genomic, proteomic and physiological response data. This "raw" data must be transformed into actionable knowledge to guide and accelerate researchers' investigations for new vaccines, adjuvants, and immunotherapeutic drugs. Seralogix's new computational methods for identifying genetically regulated host immune response will significantly aid in advancing our understanding of the molecular targets and immunologic mechanisms critical to robust defense against pathogenic microbes. This is relevant, not only to biodefense, but for many other pathogens and diseases -- especially in studies where it is important to eventually predict human response from animal models. Seralogix believes that such tools will be capable of discovering novel immunologic mechanism and thus have significant commercial potential. [unreadable] [unreadable] [unreadable]